{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras import applications\n",
    "from keras.models import Sequential\n",
    "from keras.models import Model\n",
    "from keras.layers import Dropout, Flatten, Dense, Activation, Reshape, LeakyReLU\n",
    "from keras.callbacks import CSVLogger\n",
    "import tensorflow as tf\n",
    "from scipy.ndimage import imread\n",
    "import numpy as np\n",
    "import random\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Conv1D, MaxPooling1D\n",
    "from keras import backend as K\n",
    "import keras\n",
    "from keras.callbacks import CSVLogger, ModelCheckpoint\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "from keras import optimizers\n",
    "import h5py\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import pandas as pd\n",
    "# import matplotlib\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with h5py.File(''.join(['bitcoin2015to2017_close.h5']), 'r') as hf:\n",
    "    datas = hf['inputs'].value\n",
    "    labels = hf['outputs'].value\n",
    "    input_times = hf['input_times'].value\n",
    "    output_times = hf['output_times'].value\n",
    "    original_inputs = hf['original_inputs'].value\n",
    "    original_outputs = hf['original_outputs'].value\n",
    "    original_datas = hf['original_datas'].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL']='2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler()\n",
    "#split training validation\n",
    "training_size = int(0.8* datas.shape[0])\n",
    "training_datas = datas[:training_size,:,:]\n",
    "training_labels = labels[:training_size,:,0]\n",
    "validation_datas = datas[training_size:,:,:]\n",
    "validation_labels = labels[training_size:,:,0]\n",
    "validation_original_outputs = original_outputs[training_size:,:,:]\n",
    "validation_original_inputs = original_inputs[training_size:,:,:]\n",
    "validation_input_times = input_times[training_size:,:,:]\n",
    "validation_output_times = output_times[training_size:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true = np.append(validation_original_inputs,validation_original_outputs, axis=1)\n",
    "ground_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, 272, 1)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_times = np.append(validation_input_times,validation_output_times, axis=1)\n",
    "ground_true_times.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "step_size = datas.shape[1]\n",
    "batch_size= 8\n",
    "nb_features = datas.shape[2]\n",
    "epochs = 1\n",
    "output_size=16\n",
    "units= 50\n",
    "second_units=30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(units=units,activation='tanh', input_shape=(step_size,nb_features),return_sequences=False))\n",
    "#model.add(LSTM(units=second_units,activation='relu',return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(output_size))\n",
    "model.add(LeakyReLU())\n",
    "model.load_weights('weights/bitcoin2015to2017_close_LSTM_1_tanh_leaky_-44-0.00004.hdf5')\n",
    "model.compile(loss='mse', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300628, 1)"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_datas.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 221, 16)\n",
      "(3536,)\n"
     ]
    }
   ],
   "source": [
    "predicted = model.predict(validation_datas)\n",
    "predicted_inverted = []\n",
    "\n",
    "# In[7]:\n",
    "# we only care about the 0 axis, close price data\n",
    "\n",
    "scaler.fit(original_datas[:,0].reshape(-1,1))\n",
    "predicted_inverted.append(scaler.inverse_transform(predicted))\n",
    "print np.array(predicted_inverted).shape\n",
    "#get only the close data\n",
    "ground_true = ground_true[:,:,0].reshape(-1)\n",
    "ground_true_times = ground_true_times.reshape(-1)\n",
    "ground_true_times = pd.to_datetime(ground_true_times, unit='s')\n",
    "# since we are appending in the first dimension\n",
    "predicted_inverted = np.array(predicted_inverted)[0,:,:].reshape(-1)\n",
    "print np.array(predicted_inverted).shape\n",
    "validation_output_times = pd.to_datetime(validation_output_times.reshape(-1), unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60112, 2)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_true_df = pd.DataFrame()\n",
    "ground_true_df['times'] = ground_true_times\n",
    "ground_true_df['value'] = ground_true\n",
    "ground_true_df.set_index('times').reset_index()\n",
    "ground_true_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3536, 2)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df = pd.DataFrame()\n",
    "prediction_df['times'] = validation_output_times\n",
    "prediction_df['value'] = predicted_inverted\n",
    "prediction_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-06-04 13:50:00</td>\n",
       "      <td>2489.749268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-06-04 13:55:00</td>\n",
       "      <td>2478.548584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-06-04 14:00:00</td>\n",
       "      <td>2465.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-06-04 14:05:00</td>\n",
       "      <td>2467.365967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-06-04 14:10:00</td>\n",
       "      <td>2480.132812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                times        value\n",
       "0 2017-06-04 13:50:00  2489.749268\n",
       "1 2017-06-04 13:55:00  2478.548584\n",
       "2 2017-06-04 14:00:00  2465.910400\n",
       "3 2017-06-04 14:05:00  2467.365967\n",
       "4 2017-06-04 14:10:00  2480.132812"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prediction_df = prediction_df.loc[(prediction_df[\"times\"].dt.year == 2017 )&(prediction_df[\"times\"].dt.month > 7 ),: ]\n",
    "ground_true_df = ground_true_df.loc[(ground_true_df[\"times\"].dt.year == 2017 )&(ground_true_df[\"times\"].dt.month > 7 ),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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9IlACAAAAAGSsnqAaQPNOmpy4juMsjD22pysQEvXlg/ZOXf/Hl3T9HzdI8i7Z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\ny1r7rqTNkj7u+9lsrd1irT0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enZ5/oEZmDpLNzJSpq+vy+S2SUhS+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mCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcbc49e75d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(ground_true_df.times,ground_true_df.value, label = 'Actual')\n",
    "# plt.plot(prediction_df.times,prediction_df.value, label = 'Predicted')\n",
    "plt.plot(prediction_df.times,prediction_df.value,'ro', label='Predicted')\n",
    "plt.legend(loc='upper left')\n",
    "plt.savefig('result/bitcoin2015to2017_close_LSTM_1_tanh_leaky_result.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15363.599112060345"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(validation_original_outputs[:,:,0].reshape(-1),predicted_inverted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
